Cooperative Caching Layer Overview

• Cooperative caching layer is a distributed architecture that coordinates local and multicast caching to reduce load and optimize wireless data delivery.
• It employs MDS-coded caching and subpacketization techniques to ensure reliable data recovery even when certain nodes act selfishly.
• The design achieves provable load optimality while significantly reducing subpacketization overhead and adapting to varied network conditions.

A cooperative caching layer is a specialized architectural and algorithmic construct that implements distributed, collaborative storage and retrieval of content across multiple devices, base stations, or network nodes. In wireless and D2D networks, such layers enable nodes not only to cache files locally, but also to participate in coordinated placement, discovery, and multicast transmission protocols. This coordination can dramatically reduce transmission loads, optimize capacity, mitigate the impact of non-cooperating (“selfish”) nodes, and, in the case of coded or hierarchical schemes, achieve provable information-theoretic optimality. The cooperative caching layer concept is fundamental for enabling high-throughput, low-latency, and cost-efficient wireless data services under practical energy, memory, and participation constraints, as most rigorously analyzed in "A Novel Coded Caching Scheme for Partially Cooperative Device-to-Device Networks" (T. et al., 2 Sep 2025).

1. Network and User Model

Consider a network with $K$ users, each equipped with cache memory of size $M$ files, accessing a library of $N$ files (file size $B$ symbols). The network distinguishes between non-selfish (transmitting) and selfish (non-transmitting) users: $S$ out of $K$ users may refrain from transmission during delivery, yet all users simultaneously make requests. Define $\mathcal{U}$ as the set of all users, $\mathcal{U}_S$ as the selfish subset, and $\mathcal{K} = \mathcal{U} \setminus \mathcal{U}_S$ as the transmitting subset, with $|\mathcal{K}| = K-S$. A necessary feasibility condition is: $M \ge \frac{N}{K - S}$ ensuring that the collective caches of the non-selfish nodes suffice to reconstruct any demand vector (T. et al., 2 Sep 2025).

2. Coded Cache Placement and Subpacketization

The cooperative caching layer leverages MDS-coded caching to guarantee decodability for any demand profile, irrespective of which users turn out selfish. Placement proceeds as follows:

• Fix a coded subpacketization parameter $t \in \{0,1, \dots, K-1\}$.
• Each file $W_n$ is split into

$F = (K-S)\binom{K-1}{t} + S\binom{K-2}{t-1}$

subpackets, which are then encoded via a $[K\binom{K-1}{t}, F]$ MDS code.

• Each codeword symbol is identified by $(k,\mathcal{T})$ with $k$ a user and $\mathcal{T} \subseteq [K]\setminus\{k\}$, $|\mathcal{T}|=t$.
• User $U_k$ stores all symbols $Y_{n, \mathcal{T}^{(k)}}$ (as above) and also those $Y_{n,\mathcal{T}^{(\ell)}}$ with $\ell \neq k$, $k \in \mathcal{T}$, $|\mathcal{T}| = t$.

The cache occupancy per user is shown to match the available capacity: $|Z_k| = BM = B \frac{N(t+1)(K-1)}{(K-S)(K-1) + tS}$ Subpacketization $F$ is explicitly reduced relative to prior cooperative schemes, especially for moderate $S$ values (T. et al., 2 Sep 2025).

3. Cooperative Multicast Delivery

Delivery uses coded multicasting exploiting the non-selfish users only:

• Each transmitter forms, for every $(t+1)$-subset $\mathcal{S} \subseteq [K] \setminus \{k\}$,

$$X_{k,\mathcal{S}} = \bigoplus_{s\in \mathcal{S}} Y_{d_s, \mathcal{S}\setminus\{s\}}^{(k)}$$

Each such coded packet allows all members of $\mathcal{S}$ to recover a new segment of their requested file.

• Total number of packets per transmitter is $\binom{K-1}{t+1}$, and the normalized overall load is

$$R(M,S) = \frac{(K-S)(K-1)}{(K-S)(K-1) + tS} \frac{K-t-1}{t+1}$$

(T. et al., 2 Sep 2025). This is performed in one shot, requiring at most one transmission per transmitter per user.

4. Decodability and Worst-Case Load

All users, including selfish ones, collect exactly $F$ independent coded symbols of their requested files—those stored in their caches plus what is received in delivery. MDS decoding ensures exact recovery. Critically, the layer achieves "one-shot" delivery: no user requires repeated transmissions from any transmitter across requests (T. et al., 2 Sep 2025).

The scheme is rigorously compared to a cut-set lower bound. For any partially cooperative scheme with $S$ selfish users,

$$R^*(M) \ge \max_{s=1,\dots,K-1} \frac{N - sM}{\frac{\max(K-S-s, 1)}{K-S} \lceil N/s \rceil}$$

with simplification for $s = 1$ yielding

$R^*(M) \ge \frac{K-S}{K-S-1}(1 - M/N)$

and the scheme matches this bound (i.e., becomes information-theoretically optimal) in the high-memory regime.

5. Robustness to Non-cooperation and Subpacketization Trade-off

Unlike schemes that require advance knowledge of which users will be selfish at placement time, this cooperative caching design only needs the number $S$, not the identities. It operates for all $M \ge N/(K-S)$, not only in the high-memory regime, and can flexibly adapt to any pattern of partial participation (T. et al., 2 Sep 2025).

Comparison to prior art shows:

• Previous schemes may have lower load when $M \ge N(S+1)/K$ but suffer from prohibitively high subpacketization.
• For moderate $S$ (not all users are selfish), the described MDS-code layer reduces subpacketization $F$ by orders of magnitude while maintaining or improving load-optimality (T. et al., 2 Sep 2025).

6. Generalization and Performance Characteristics

The cooperative caching layer, as constructed here, generalizes to arbitrary numbers of transmitters and selfish users, with explicit performance analysis across the full memory regime. The load-memory $R(M)$ curves, subpacketization-memory tradeoffs, and order-optimality guarantees are visualized in the cited work for various system configurations. Operating points can be tuned via the subpacketization parameter $t$ and memory-sharing optimizations to adapt to changing network conditions or policies regarding user participation (T. et al., 2 Sep 2025).

7. Implications for Wireless System Design

This cooperative caching layer sets a new standard for D2D caching, enabling robust coded multicasting under arbitrary user cooperation models. It eliminates the need for per-user participation commitments before placement, supports dynamic and privacy-limited networks, and leverages advanced subpacketization and MDS coding control for practical deployment. The framework provides a blueprint for next-generation wireless caching architectures requiring resilience to selfishness, minimal coordination, and load/subpacketization scalability (T. et al., 2 Sep 2025).